Chak Sangma

1.4k total citations
39 papers, 1.1k citations indexed

About

Chak Sangma is a scholar working on Molecular Biology, Biomedical Engineering and Infectious Diseases. According to data from OpenAlex, Chak Sangma has authored 39 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Molecular Biology, 12 papers in Biomedical Engineering and 11 papers in Infectious Diseases. Recurrent topics in Chak Sangma's work include Biosensors and Analytical Detection (8 papers), Influenza Virus Research Studies (7 papers) and Mass Spectrometry Techniques and Applications (6 papers). Chak Sangma is often cited by papers focused on Biosensors and Analytical Detection (8 papers), Influenza Virus Research Studies (7 papers) and Mass Spectrometry Techniques and Applications (6 papers). Chak Sangma collaborates with scholars based in Thailand, Austria and United States. Chak Sangma's co-authors include Peter A. Lieberzeit, Arunee Thitithanyanont, Boonsong Kongkathip, Suwaporn Luangkamin, Ngampong Kongkathip, Pongpun Siripong, Palangpon Kongsaeree, Prasert Auewarakul, Pa‐thai Yenchitsomanus and M. Paul Gleeson and has published in prestigious journals such as Analytical Biochemistry, Journal of Virology and Biochemical and Biophysical Research Communications.

In The Last Decade

Chak Sangma

39 papers receiving 1.1k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Chak Sangma Thailand 16 438 340 251 241 205 39 1.1k
Paola Corona Italy 20 370 0.8× 30 0.1× 132 0.5× 198 0.8× 86 0.4× 54 1.3k
Mavanur R. Suresh Canada 24 510 1.2× 104 0.3× 114 0.5× 319 1.3× 23 0.1× 76 1.6k
Parameswaran Saravanan India 19 306 0.7× 58 0.2× 185 0.7× 204 0.8× 9 0.0× 56 945
Waleed Younis United States 17 362 0.8× 87 0.3× 94 0.4× 332 1.4× 53 0.3× 38 961
Jianxin Chen China 15 201 0.5× 35 0.1× 111 0.4× 111 0.5× 16 0.1× 41 702
Pamela Hunter United States 13 292 0.7× 36 0.1× 189 0.8× 168 0.7× 14 0.1× 26 1.1k
Gary E. Zurenko United States 26 1.2k 2.7× 57 0.2× 429 1.7× 1.2k 5.1× 33 0.2× 65 3.4k
Lian Duan China 21 755 1.7× 229 0.7× 879 3.5× 450 1.9× 13 0.1× 45 2.1k
Michael R. Roner United States 22 234 0.5× 67 0.2× 58 0.2× 377 1.6× 6 0.0× 73 1.3k
Marion Grare France 15 467 1.1× 63 0.2× 319 1.3× 144 0.6× 34 0.2× 33 1.2k

Countries citing papers authored by Chak Sangma

Since Specialization
Citations

This map shows the geographic impact of Chak Sangma's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Chak Sangma with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chak Sangma more than expected).

Fields of papers citing papers by Chak Sangma

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chak Sangma. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Chak Sangma. The network helps show where Chak Sangma may publish in the future.

Co-authorship network of co-authors of Chak Sangma

This figure shows the co-authorship network connecting the top 25 collaborators of Chak Sangma. A scholar is included among the top collaborators of Chak Sangma based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Chak Sangma. Chak Sangma is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Thepparit, Chutima, et al.. (2025). MIP-based electrochemical sensor with machine learning for accurate ZIKV detection in protein- and glucose-rich urine. Analytical Biochemistry. 702. 115854–115854. 5 indexed citations
2.
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Pintavirooj, Chuchart, et al.. (2022). Biosensors for Klebsiella pneumoniae with Molecularly Imprinted Polymer (MIP) Technique. Sensors. 22(12). 4638–4638. 15 indexed citations
5.
Thitithanyanont, Arunee, et al.. (2022). Virus MIP-composites for SARS-CoV-2 detection in the aquatic environment. Materials Letters. 315. 131973–131973. 24 indexed citations
6.
Sangma, Chak, et al.. (2021). Enhancing sensitivity of QCM for dengue type 1 virus detection using graphene-based polymer composites. Analytical and Bioanalytical Chemistry. 413(24). 6191–6198. 13 indexed citations
7.
Sangma, Chak, et al.. (2017). H5N1 Virus Plastic Antibody Based on Molecularly Imprinted Polymers. Methods in molecular biology. 1575. 381–388. 6 indexed citations
8.
Yenchitsomanus, Pa‐thai, et al.. (2017). Small‐Molecule Dengue Virus Co‐imprinting and Its Application as an Electrochemical Sensor. ChemistryOpen. 6(3). 340–344. 11 indexed citations
9.
Warakulwit, Chompunuch, et al.. (2016). A novel method for dengue virus detection and antibody screening using a graphene-polymer based electrochemical biosensor. Nanomedicine Nanotechnology Biology and Medicine. 13(2). 549–557. 83 indexed citations
10.
Thitithanyanont, Arunee, et al.. (2013). Influenza A virus molecularly imprinted polymers and their application in virus sub-type classification. Journal of Materials Chemistry B. 1(16). 2190–2190. 75 indexed citations
11.
Sangma, Chak, et al.. (2013). Self-assembled glucosamine monolayers as biomimetic receptors for detecting WGA lectin and influenza virus with a quartz crystal microbalance. Analytical and Bioanalytical Chemistry. 405(20). 6471–6478. 22 indexed citations
12.
Hannongbua, Supa, et al.. (2011). In silico screening of epidermal growth factor receptor (EGFR) in the tyrosine kinase domain through a medicinal plant compound database. Journal of Molecular Modeling. 18(3). 1241–1254. 17 indexed citations
13.
Sangma, Chak, et al.. (2011). Receptor recognition mechanism of human influenza A H1N1 (1918), avian influenza A H5N1 (2004), and pandemic H1N1 (2009) neuraminidase. Journal of Molecular Modeling. 18(1). 285–293. 8 indexed citations
14.
Ubol, S, Ampa Suksatu, Naphak Modhiran, et al.. (2010). Intra-host diversities of the receptor-binding domain of stork faeces-derived avian H5N1 viruses and its significance as predicted by molecular dynamic simulation. Journal of General Virology. 92(2). 307–314. 3 indexed citations
15.
Sangma, Chak, et al.. (2010). Computer Techniques for Drug Development from Thai Traditional Medicine. Current Pharmaceutical Design. 16(15). 1753–1784. 3 indexed citations
16.
Auewarakul, Prasert, Ornpreya Suptawiwat, Alita Kongchanagul, et al.. (2007). An Avian Influenza H5N1 Virus That Binds to a Human-Type Receptor. Journal of Virology. 81(18). 9950–9955. 165 indexed citations
17.
Kongkathip, Boonsong, Chak Sangma, Kanyawim Kirtikara, et al.. (2005). Inhibitory effects of 2-substituted-1-naphthol derivatives on cyclooxygenase I and II. Bioorganic & Medicinal Chemistry. 13(6). 2167–2175. 8 indexed citations
18.
Saparpakorn, Patchreenart, Chak Sangma, Pornwaratt Niyomrattanakit, et al.. (2005). Competitive inhibition of the dengue virus NS3 serine protease by synthetic peptides representing polyprotein cleavage sites. Biochemical and Biophysical Research Communications. 330(4). 1237–1246. 67 indexed citations
19.
Sangma, Chak, et al.. (2005). Virtual Screening for Anti-HIV-1 RT and Anti-HIV-1 PR Inhibitors from the Thai Medicinal Plants Database: A Combined Docking with Neural Networks Approach. Combinatorial Chemistry & High Throughput Screening. 8(5). 417–429. 16 indexed citations
20.
Kongkathip, Ngampong, Boonsong Kongkathip, Pongpun Siripong, et al.. (2003). Potent antitumor activity of synthetic 1,2-Naphthoquinones and 1,4-Naphthoquinones. Bioorganic & Medicinal Chemistry. 11(14). 3179–3191. 201 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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